Anti-steganalysis for image on convolutional neural networks

  • Shiyu Li
  • Dengpan YeEmail author
  • Shunzhi Jiang
  • Changrui Liu
  • Xiaoguang Niu
  • Xiangyang Luo


Nowadays, convolutional neural network (CNN) based steganalysis methods achieved great performance. While those methods are also facing security problems. In this paper, we proposed an attack scheme aiming at CNN based steganalyzer including two different attack methods 1) the LSB-Jstego Gradient Based Attack; 2) LSB-Jstego Evolutionary Algorithms Based Attack. The experiment results show that the attack strategies could achieve 96.02% and 90.25% success ratio separately on the target CNN. The proposed attack scheme is an effective way to fool the CNN based steganalyzer and in addition demonstrates the vulnerability of the neural networks in steganalysis.


Anti-steganalysis CNN Adversarial example 



An earlier version of this paper was presented at the 4th International Conference on Cloud Computing and Security, 8-10, June 2018, Haikou, China. This work was partially supported by the National Key Research Development Program of China (2016QY01W0200), the National Natural Science Foundation of China NSFC (U1636101,U1636219, U1736211).


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Shiyu Li
    • 1
  • Dengpan Ye
    • 1
    Email author
  • Shunzhi Jiang
    • 1
  • Changrui Liu
    • 1
  • Xiaoguang Niu
    • 2
  • Xiangyang Luo
    • 3
  1. 1.School of Cyber Science and EngineeringWuhan UniversityWuhanChina
  2. 2.School of Computer ScienceWuhan UniversityWuhanChina
  3. 3.State Key Laboratory of Mathematical Engineering and Advanced ComputingZhengzhouChina

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